Simulation of anisotropic chemical etching of crystalline silicon using a cellular automata model
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2D Analysis and simulation of quartz crystal etch penetration by revisiting a previous geometric method
2024, Sensors and Actuators A: PhysicalEtch and growth rates of GaN for surface orientations in the <0001> crystallographic zone: Step flow and terrace erosion/filling via the Continuous Cellular Automaton
2023, Materials Science in Semiconductor ProcessingCitation Excerpt :In order to describe the orientation dependence of the etch rate previous work by Zubel focused on the classification of the surface bonds instead of the surface atoms [21,22]. However, the resulting etch rates for crystalline silicon are exactly equal to those derived using a basic (or discrete) Cellular Automaton (BCA or DCA), which assigns the surface atoms with removal rates of value 1 (for most atoms) or 0 (for terrace monohydrides) [23–25]. Unfortunately, the BCA (and, thus, Zubel’s model) has very limited applicability (e.g., 30–40 wt% KOH on Si{100} wafers only) in comparison to the CCA, whose removal rates can take any value (not limited to 1 or 0), leading to successful simulations and modeling for many etchants, substrates and substrate orientations [18–20].
Review of atomic layer deposition process, application and modeling tools
2022, Materials Today: ProceedingsCitation Excerpt :Only the primary direction rate is examined in the Ordinary-CA approach, which assumes only two fixed states of atoms on the lattice. Than and Buttgenbach [68] proposed the Random-CA approach. A probability parameter P determines whether the pre-deposited atom completed the opportunity procedure.
Modeling of silicon etching
2020, Handbook of Silicon Based MEMS Materials and TechnologiesWet etching of silicon
2020, Handbook of Silicon Based MEMS Materials and TechnologiesA synchronous cellular automaton model of mass transport in porous media
2016, Computers and Chemical EngineeringCitation Excerpt :The main issue thereby is to establish reasonable transition rules for a given process (Richards et al., 1990). For some mass transfer problems such rules have been formulated to study drug release (Zygourakis and Markenscoff, 1996; Bertrand et al., 2007; Laaksonen et al., 2009), diffusion of ligands across the protein surface (Kier et al., 2003), transport through a chromatographic column (Kier et al., 2000), chemical etching and corrosion (Than and Büttgenbach, 1994; Córdoba-Torres et al., 2001). Ideally, the set of rules should not explicitly describe every possible response of the model, i.e. not be phemonological.